Titlebook: Linear Models and Generalizations; Least Squares and Al C. Radhakrishna Rao,Shalabh,Christian Heumann Textbook 2008Latest edition Springer-

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書目名稱Linear Models and Generalizations影響因子(影響力)




書目名稱Linear Models and Generalizations影響因子(影響力)學(xué)科排名




書目名稱Linear Models and Generalizations網(wǎng)絡(luò)公開度




書目名稱Linear Models and Generalizations網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Linear Models and Generalizations被引頻次




書目名稱Linear Models and Generalizations被引頻次學(xué)科排名




書目名稱Linear Models and Generalizations年度引用




書目名稱Linear Models and Generalizations年度引用學(xué)科排名




書目名稱Linear Models and Generalizations讀者反饋




書目名稱Linear Models and Generalizations讀者反饋學(xué)科排名

Exposition

Springer Series in Statisticshttp://image.papertrans.cn/l/image/586344.jpg

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arrogant

The Multiple Linear Regression Model and Its Extensions,The main topic of this chapter is the linear regression model with more than one independent variables. The principles of . and . are used for the estimation of parameters. We present the algebraic, geometric, and statistical aspects of the problem, each of which has an intuitive appeal.

語源學(xué)

Hemoptysis

寬宏大量

Prediction in the Generalized Regression Model,68, 1970a, 1970b, 1970c). One of the main aims of the above publications is to examine the conditions under which biased estimators can lead to an improvement over conventional unbiased procedures. In the following, we will concentrate on recent results connected with alternative superiority criteria.

熔巖

Sensitivity Analysis,en values of regressor variables. Methods for detecting outliers and deviation from normality of the distribution of errors are given in some detail. The material of this chapter is drawn mainly from the excellent book by Chatterjee and Hadi (1988).

Range-Of-Motion

Hallmark

Models for Categorical Response Variables,ationship between the expectation of a response variable and unknown predictor variables according to . The parameters are estimated according to the principle of least squares and are optimal according to minimum dispersion theory, or in case of a normal distribution, are optimal according to the ML theory (cf. Chapter 3).